The ABCD of Kidney Allograft Pathology—The Beginning of the Beginning
Authors: Thangamani Muthukumar, Dany Anglicheau
Journal: Journal of the American Society of Nephrology (2022)

Artificial intelligence (AI) is a field that combines computer science and robust datasets to enable problem solving. Big data are information whose scale, diversity, and complexity require specific technology and analytical methods to manage and extract value and hidden knowledge from them.1 Computational pathology is the analysis of pathology data and metadata using a broad array of methods to extract patterns and explore features.2 Digital pathology involves tools and systems to digitize pathology slides. This ABCD combination is transforming the pathology ecosystem; nephropathology is no exception. Modern medicine is conventionally hypothesis driven, where a question is asked, an explanation is provided, and controlled experiments are designed and done that result in either refinement or rejection of the explanation. With advancements in handling big data, the post-2001 Human Genome Project era has challenged this paradigm. Data-driven approaches, instead of starting with a hypothesis, attempt to determine multiple aspects of the data to best categorize the data, thus allowing it to speak for itself. This hypothesis- versus data-driven conundrum is analogous to the frequentist versus Bayesian approaches to statistical inference. The former begins by assuming a null hypothesis to be true and asks how probable these data are given the null hypothesis. The latter asks how probable a hypothesis is given these data. Big data is typically analyzed by machine learning (ML) methods—a branch of AI that analyzes data on the basis of algorithms, learns salient relationships among variables, and uses that knowledge to make predictions on new data, progressively improving with repeated data exposure. There are two broad ML approaches, supervised and unsupervised. A semisupervised technique is a hybrid.3 Although conventional regression models are derived on the basis of theory and assumptions and benefit from human intervention and subject knowledge for model specification, ML focuses on models that directly and automatically learn from data.4 Cluster analysis and archetypal analysis are common algorithms for unsupervised learning. K means is a popular clustering algorithm that partitions the data into k-predefined distinct clusters and tries to make the data points within a cluster as similar as possible while also keeping the different clusters as far as possible. Uniform manifold approximation and projection are types of cluster analyses that are widely applied to single-cell RNA-sequencing data. Deep learning is an ML method that structures algorithms in layers, like human neural circuits. Convolutional neural network is a deep learning method for processing data that have a grid-like topology. The ML algorithm's training data are called a training set; the algorithm builds a mathematical model by an iterative process on the basis of the input data. A trained model is then validated on a validation set. The trained validated model is eventually evaluated on a third dataset called the test set. Standardizing the reporting of histopathologic features of kidney allograft rejection has always been a challenge. Banff classification, first published in 1993, has been widely accepted by the transplant community.5 The criteria, initially on the basis of expert opinions, have evolved and continue to evolve. However, the Banff lesion scores are not specific to a disease process, and diagnostic categories are not mutually exclusive. Therefore, biopsies with intermediate, mixed, and indeterminate phenotypes make therapeutic decisions challenging. Could ML that is on the basis of Banff lesion scores improve the classification? The Banff 2019 conference, in addition to refining and simplifying the classification, emphasized the usage of AI in transplant pathology.6Table 1 summarizes key kidney allograft–pathology studies that utilized one or more of the ABCD components. Table 1. - Kidney allograft pathology studies that utilized one or more of the ABCD components PubMed Unique Identifier Author/Year/DOI Journal Biopsy Samples Algorithm Major Findings 10583562 Furness et al., 1999doi:10.1046/j.1365-2559.1999.035005461.x Histopathology Training: 100; validation: 20 random from the 100; testing: 46 Neural network of Banff lesion scores Considerable improvement in the diagnosis of early acute rejection 30506642 Matas et al., 2019doi:10.1111/ajt.15204 American Journal of Transplantation Cases: 265 with for-cause biopsies Hierarchical cluster analysis of Banff lesion scores Six major clusters identified with increasing differentiation in graft failure rate during long-term follow-up 30872323 Aubert et al., 2019doi:10.1681/ASN.2018070777 JASN Cases: 552 with transplant glomerulopathy from 385 patients Archetypal analysis Five archetypes identified with distinct functional, immunologic, and histologic features and distinct allograft survival profiles 30947386 Denic et al., 2019doi:10.1111/ajt.15380 American Journal of Transplantation Cases: graft failure (n=67) after a 5-year surveillance biopsy; controls: age and sex-matched with functioning allograft (n=134) after a 5-year surveillance biopsy Computer-assisted morphometrics of scanned whole-slide images Computer-assisted morphometrics-based models predict graft loss better than Banff lesion score-based models 34757124 Yi et al., 2022doi:10.1016/j.kint.2021.09.028 Kidney International Training: 60 slides of transplant biopsies (17,470 object images); validation: 10-fold crossvalidation; testing: 33 slides of transplant biopsies (5222 object images); clinical investigation: whole-slide images from 478 preimplantation biopsies and 311 post-transplant 12-mo surveillance biopsies Convolutional neural networks Interstitial and Tubular Abnormality Score in baseline samples predicted 1-yr graft loss, Composite Damage Score in 12-mo post-transplant biopsies predicted later graft loss, and both scores outperformed Banff scores or clinical predictors 34794930 Kers et al., 2022doi:10.1016/S2589-7500(21)00211-9 Lancet Digital Health Training: 5541 digital whole-slide images from 1847 biopsies; validation: three-fold crossvalidation; test: 303 whole-slide images from 101 biopsies Convolutional neural networks Convolutional neural networks classified biopsies with reasonable accuracy 35843265 Hermsen et al., 2022doi:10.1016/j.ajpath.2022.06.009 American Journal of Pathology 99 whole-slide images for the development of the structure segmentation network, including training: 63 whole-slide images; validation: 16 whole-slide images; and test: 20 whole-slide images; clinical investigation: 125 whole-slide images from 125 biopsies from 73 patients Convolutional neural networks Convolutional neural networks provided quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and nonscarred kidney parenchyma Vaulet et al.,7 in a paper published in JASN in 2021, used a training set of 3510 kidney allograft biopsies from 936 recipients and studied five Banff acute lesion scores—i, t, g, ptc, and v—as well as peritubular capillary C4d deposits and thrombotic microangiopathy (yes/no). Chronic lesions were ignored. Donor-specific IgG anti-HLA antibody (DSA; yes/no) information was included. Six clusters were defined using a semisupervised clustering algorithm that ingeniously introduced graft survival information into the clustering process by Cox scores that weighted Banff acute lesions. Cluster 1 had limited inflammation, cluster 2 had moderate to severe glomerulitis, and cluster 3 had moderate to severe tubulointerstitial inflammation. All three clusters had no DSA. The remaining three clusters had DSA; cluster 4 had limited inflammation but with C4d in some, cluster 5 had a high g score, and cluster 6 had high t and i scores combined with g and ptc scores. Although there was a resemblance to Banff categories, this data-driven approach eliminated intermediate and mixed phenotypes. Cluster-based compared with Banff categories were better in discriminating graft failure from no graft failure. Because the association between clusters and outcome could be influenced by their choice of a semisupervised clustering approach (dependent on certain human decisions rather than entirely data driven), the authors validated their findings in an external validation cohort of 3835 biopsies from two centers. In the current issue of JASN, the same team extends their data-driven approach to reclassifying kidney allograft biopsies on the basis of chronic lesion scores.8 Using the same data, they focus on chronic Banff lesion scores—cg, ci, ct, cv, mm, and ah—as well as glomerulosclerosis. Because chronic lesions could change over time, these lesions received time-dependent weights for their association with graft failure. Four clusters were defined: cluster 1 dominated by early biopsies with minimal chronic lesion scores; cluster 2 with higher ci and ct scores than cluster 1, cluster 3 with severe ci and ct scores along with cv, ah, and glomerulosclerosis and mostly cg0 scores; and cluster 4 with mild to severe cg scores. Clusters 3 and 4 were associated with an increased risk of graft failure compared with cluster 1. When visualized on a two-dimensional polar plot, the radius represents the total chronicity score of the biopsy. A combined model of chronic and acute radii was better in discriminating graft failure from no graft failure. An additional, perhaps counterintuitive finding, is the independence of the acute from chronic lesions, suggesting that both acute and chronic prediction models should be assessed in parallel, not together, on the same biopsy. The studies by Vaulet et al.7,8 provide much needed mathematical validity to the Banff classification. Is AI-based clustering better than natural intelligence–based classifications? We will need a clear demonstration of the utility of AI-based approaches in terms of improved clinical decision making. Meaningful clinical validation of clustering methods would require predefined therapeutic approaches driven by AI-based methods. If Banff lesion scores are poorly reproducible,9,10 are models built on it any good? It is our expectation that as AI tools grow, standardization and reproducibility of Banff lesion scores would improve, and along with multiomics-based precision medicine approaches, result in the endorsement of data-driven methodologies. Clinician-scientists and transplant pathologists, in addition to training their fellows, would need to train AI-based models. This is the beginning of the beginning but the right way to go! Disclosures D. Anglicheau reports consultancy agreements with Astellas, BMS, Chiesi, Novartis, and Sandoz; research funding from Astellas, BMS, Chiesi, and Novartis; and honoraria from BMS and Novartis. T. Muthukumar reports an advisory or leadership role with Kidney International Reports Editorial Board and research funding from the National Institutes of Health. Funding None.